Image Processing for Fundus Image Classification using Deep Learning
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Abstract
This paper proposed the using a computer for classifying the diabetic retinopathy 4 diabetic severity levels: normal level, light level, medium level and severe level from the fundus image by using image processing with the deep learning. The development of the model for classification of fundus images, shown that the modeling of this paper is more accurate than previous research using machine learning. In addition, this paper uses the model developed to be a prototype. It is shown that the accuracy of the classification of the severity of the diabetic retinopathy, which can help the ophthalmologist effectively diagnose the severity of the diabetic retinopathy from the fundus image.
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References
D. Complications. (2019, Cited 1 October 2019). Diabetic Retinopathy. Available: http://www.diabetes .co.uk/diabetes-complications/diabetic-retinopathy.html [Accessed: July 20, 2020].
M. Bishop, "Pattern Recognition and Machine Learning," Springer, pp. 1–758, 2006.
L. Deng and D. Yu, "Deep Learning: Methods and Applications," Foundations and Trends in Signal Processing, pp. 1–199, 2014.
R. Keys, "Cubic convolution interpolation for digital image processing," IEEE Transactions on Acoustics, Speech, and Signal Processing, pp. 1153 - 1160, 1981.
R. Hirsch, "Exploring Colour Photography: A Complete Guide," Laurence King Publishing, pp. 1–360, 2004.
S. Johnson, "Stephen Johnson on Digital Photography," O'Reilly, pp. 1–305, 2006.
N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886–893, 2005.
J. Quinlan, "C4.5: Programs for Machine Learning," ed: Morgan Kaufmann Publishers, 1993.
T. Daniel, "Discovering Knowledge in Data," A JOHN WILEY & SONS, 2005.
K. Crammer and Y. Singer, "On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines," Journal of Machine Learning Research, pp. 265–292, 2001.
M. Hassoun, "Fundamentals of Artificial Neural Networks (MIT Press)," A Bradford Book, 2003.
E. Humphrey and J. Bello, "Rethinking Automatic Chord Recognition with Convolution Neural Networks," in Proceedings of 11th International Conference on Machine Learning and Application, 2012.
B. Tilmann, "The Business Impact of Predictive Analytics," ed: IGI Global, pp. 118–119, ISBN 978-1-59904-252-7, 2007.
R. Mahmood and B. Ghimire, "Automatic detection and classification of Alzheimer' s Disease from MRI scans using principal component analysis and artificial neural networks," in Proceedings of the 20th International Conference on Systems Signals and Image Processing, pp. 133-137, 2013.
S. Sarraf and G. Tofighi, "Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data," in Proceedings of Future Technologies Conference, San Francisco, CA, USA, pp. 816-820, 2016.
Y. Han and M. Zhao, "A hybrid sequential feature selection approach for the diagnosis of Alzheimer's Disease," in Proceedings of International Joint Conference on Neural Networks, pp. 1216-1220, 2016.
Y. Jindaluang, "Risk Factors for Diabetic Retinopathy in Diabetic Patients, Tak Municipal Area," Buddhachinaraj Medical Journal, pp. 53-61, 2009.
A. Paisal and T. Kasetkasem, "Separation the mingling varieties of the mungbean seeds by image processing," Khon Kaen Agriculture Journal, pp. 240-247, 2011.
T. Tathawee, S. Prasarnpun, S. Onbua, T. Pinthong, and A. Suwannakom, "Orchid identification based on computer vision analysis," in Proceedings of The 6th National Science Research Conference, Thailand, pp. 47 - 56, 2014.
D. Muntham and L. Ingsrisawang, "An Application of Decision Tree Algorithms for Diagnosis of the Respiratory System: A Case Study of Pranakorn Sri Ayudthaya Hospital," Journal of Health Systems Research, Vol. 4, pp. 73-81, 2010.
S. Phimphisan, "Application of Data Mining for Diabetic Retinopathy Using Decision Tree," in Journal of Srivanalai Vijai, 2016.
A. Nain, S. Paul, and M. Maynard-Reid. (13 May 2020). Keras. Available: https://keras.io/ [Accessed: July 20, 2020].
T. Team. (13 May 2020). TensorFlow. Available: https://www.tensorflow.org/ [Accessed: July 20, 2020].
R. Kohavi, "A study of crossvalidation and bootstrap for accuracy estimation and model selection," in Proceedings of the Fourteenth International joint conference on Artificial Intelligence, Montreal, Canada, pp. 1137-1143, 1995.